import math
import random

import torch
from torch import nn
from torch.nn import functional as F

from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d


class PixelNorm(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input):
        return input * torch.rsqrt(
            torch.mean(input**2, dim=1, keepdim=True) + 1e-8)


def make_kernel(k):
    k = torch.tensor(k, dtype=torch.float32)
    if k.ndim == 1:
        k = k[None, :] * k[:, None]
    k /= k.sum()
    return k


class Upsample(nn.Module):
    def __init__(self, kernel, factor=2):
        super().__init__()
        self.factor = factor
        kernel = make_kernel(kernel) * (factor**2)
        self.register_buffer('kernel', kernel)
        p = kernel.shape[0] - factor
        pad0 = (p + 1) // 2 + factor - 1
        pad1 = p // 2
        self.pad = (pad0, pad1)

    def forward(self, input):
        out = upfirdn2d(input,
                        self.kernel,
                        up=self.factor,
                        down=1,
                        pad=self.pad)
        return out


class Blur(nn.Module):
    def __init__(self, kernel, pad, upsample_factor=1):
        super().__init__()
        kernel = make_kernel(kernel)
        if upsample_factor > 1:
            kernel = kernel * (upsample_factor**2)
        self.register_buffer('kernel', kernel)
        self.pad = pad

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, pad=self.pad)
        return out


class EqualLinear(nn.Module):
    def __init__(self,
                 in_dim,
                 out_dim,
                 bias=True,
                 bias_init=0,
                 lr_mul=1,
                 activation=None):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
        else:
            self.bias = None
        self.activation = activation
        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
        self.lr_mul = lr_mul

    def forward(self, input):
        if self.activation:
            out = F.linear(input, self.weight * self.scale)
            out = fused_leaky_relu(out, self.bias * self.lr_mul)
        else:
            out = F.linear(input,
                           self.weight * self.scale,
                           bias=self.bias * self.lr_mul)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
        )


class ModulatedConv2d(nn.Module):
    def __init__(self,
                 in_channel,
                 out_channel,
                 kernel_size,
                 style_dim,
                 demodulate=True,
                 upsample=False,
                 downsample=False,
                 blur_kernel=[1, 3, 3, 1]):
        super().__init__()
        self.eps = 1e-8
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.upsample = upsample
        self.downsample = downsample

        if upsample:
            factor = 2
            p = (len(blur_kernel) - factor) - (kernel_size - 1)
            pad0 = (p + 1) // 2 + factor - 1
            pad1 = p // 2 + 1

            self.blur = Blur(blur_kernel,
                             pad=(pad0, pad1),
                             upsample_factor=factor)

        if downsample:
            factor = 2
            p = (len(blur_kernel) - factor) + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2
            self.blur = Blur(blur_kernel, pad=(pad0, pad1))

        fan_in = in_channel * kernel_size**2
        self.scale = 1 / math.sqrt(fan_in)
        self.padding = kernel_size // 2
        self.weight = nn.Parameter(
            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size))
        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
        self.demodulate = demodulate

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
            f'upsample={self.upsample}, downsample={self.downsample})')

    def forward(self, input, style):
        batch, in_channel, height, width = input.shape
        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
        weight = self.scale * self.weight * style

        if self.demodulate:
            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
        weight = weight.view(batch * self.out_channel, in_channel,
                             self.kernel_size, self.kernel_size)

        if self.upsample:
            input = input.view(1, batch * in_channel, height, width)
            weight = weight.view(batch, self.out_channel, in_channel,
                                 self.kernel_size, self.kernel_size)
            weight = weight.transpose(1, 2).reshape(batch * in_channel,
                                                    self.out_channel,
                                                    self.kernel_size,
                                                    self.kernel_size)
            out = F.conv_transpose2d(input,
                                     weight,
                                     padding=0,
                                     stride=2,
                                     groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
            out = self.blur(out)

        elif self.downsample:
            input = self.blur(input)
            _, _, height, width = input.shape
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)

        else:
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
        return out


class NoiseInjection(nn.Module):
    def __init__(self):
        super().__init__()

        self.weight = nn.Parameter(torch.zeros(1))

    def forward(self, image, n_frame, use_noise=False, noise=None):
        if use_noise:
            if noise is None:
                batch, _, height, width = image.shape
                noise = image.new_empty(batch // n_frame, 1, height,
                                        width).normal_()
                noise = noise.unsqueeze(1).repeat(1, n_frame, 1, 1, 1)
                noise = noise.view(-1, 1, height, width)
            return image + self.weight * noise
        else:
            return image


class ConstantInput(nn.Module):
    def __init__(self, channel, size=4):
        super().__init__()
        self.input = nn.Parameter(torch.randn(1, channel, size, size))

    def forward(self, input):
        batch = input.shape[0]
        out = self.input.repeat(batch, 1, 1, 1)
        return out


class StyledConv(nn.Module):
    def __init__(self,
                 in_channel,
                 out_channel,
                 kernel_size,
                 style_dim,
                 upsample=False,
                 blur_kernel=[1, 3, 3, 1],
                 demodulate=True):
        super().__init__()
        self.conv = ModulatedConv2d(
            in_channel,
            out_channel,
            kernel_size,
            style_dim,
            upsample=upsample,
            blur_kernel=blur_kernel,
            demodulate=demodulate,
        )
        self.noise = NoiseInjection()
        self.activate = FusedLeakyReLU(out_channel)

    def forward(self, input, style, n_frame, use_noise=False, noise=None):
        out = self.conv(input, style)
        out = self.noise(out, n_frame, use_noise=use_noise, noise=noise)
        out = self.activate(out)
        return out


class ToRGB(nn.Module):
    def __init__(self,
                 in_channel,
                 style_dim,
                 upsample=True,
                 blur_kernel=[1, 3, 3, 1]):
        super().__init__()
        if upsample:
            self.upsample = Upsample(blur_kernel)
        self.conv = ModulatedConv2d(in_channel,
                                    3,
                                    1,
                                    style_dim,
                                    demodulate=False)
        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))

    def forward(self, input, style, skip=None):
        out = self.conv(input, style)
        out = out + self.bias
        if skip is not None:
            skip = self.upsample(skip)
            out = out + skip
        return out


class Generator(nn.Module):
    def __init__(self,
                 size,
                 style_dim,
                 n_mlp,
                 modelR=None,
                 channel_multiplier=2,
                 blur_kernel=[1, 3, 3, 1],
                 lr_mlp=0.01):
        super().__init__()
        self.size = size
        self.style_dim = style_dim
        self.modelR = modelR
        layers = [PixelNorm()]
        for i in range(n_mlp):
            layers.append(
                EqualLinear(style_dim,
                            style_dim,
                            lr_mul=lr_mlp,
                            activation='fused_lrelu'))
        self.style = nn.Sequential(*layers)

        self.channels = {
            4: 512,
            8: 512,
            16: 512,
            32: 512,
            64: 256 * channel_multiplier,
            128: 128 * channel_multiplier,
            256: 64 * channel_multiplier,
            512: 32 * channel_multiplier,
            1024: 16 * channel_multiplier,
        }

        self.input = ConstantInput(self.channels[4])
        self.conv1 = StyledConv(self.channels[4],
                                self.channels[4],
                                3,
                                style_dim,
                                blur_kernel=blur_kernel)
        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)

        self.log_size = int(math.log(size, 2))
        self.num_layers = (self.log_size - 2) * 2 + 1

        self.convs = nn.ModuleList()
        self.upsamples = nn.ModuleList()
        self.to_rgbs = nn.ModuleList()
        self.noises = nn.Module()

        in_channel = self.channels[4]

        for layer_idx in range(self.num_layers):
            res = (layer_idx + 5) // 2
            shape = [1, 1, 2**res, 2**res]
            self.noises.register_buffer(f'noise_{layer_idx}',
                                        torch.randn(*shape))

        for i in range(3, self.log_size + 1):
            out_channel = self.channels[2**i]

            self.convs.append(
                StyledConv(
                    in_channel,
                    out_channel,
                    3,
                    style_dim,
                    upsample=True,
                    blur_kernel=blur_kernel,
                ))

            self.convs.append(
                StyledConv(out_channel,
                           out_channel,
                           3,
                           style_dim,
                           blur_kernel=blur_kernel))

            self.to_rgbs.append(ToRGB(out_channel, style_dim))

            in_channel = out_channel

        self.n_latent = self.log_size * 2 - 2

    def make_noise(self):
        device = self.input.input.device
        noises = [torch.randn(1, 1, 2**2, 2**2, device=device)]
        for i in range(3, self.log_size + 1):
            for _ in range(2):
                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
        return noises

    def mean_latent(self, n_latent):
        latent_in = torch.randn(n_latent,
                                self.style_dim,
                                device=self.input.input.device)
        latent = self.style(latent_in).mean(0, keepdim=True)

        return latent

    def get_latent(self, input):
        return self.style(input)

    def forward(self,
                styles,
                n_frame,
                interpolation=False,
                return_latents=False,
                inject_index=None,
                truncation=1,
                truncation_latent=None,
                use_noise=False,
                noise=None,
                randomize_noise=True):
        styles = [self.style(s) for s in styles]
        out, rand_in, rand_rec = self.modelR(styles[0], n_frame)
        styles = [out]
        if interpolation:
            tmp = [s.view(-1, n_frame, s.shape[-1]) for s in styles]
            tmp = tmp[0]
            styles = []
            for i in range(0, n_frame - 1):
                styles.append(tmp[:, i])
                styles.append((tmp[:, i] + tmp[:, i + 1]) / 2.)
            styles.append(tmp[:, -1])
            styles = [s.unsqueeze(1) for s in styles]
            styles = torch.cat(styles, dim=1)
            styles = [styles.view(-1, styles.shape[-1])]
            n_frame = n_frame * 2 - 1

        if noise is None:
            if randomize_noise:
                noise = [None] * self.num_layers
            else:
                noise = [
                    getattr(self.noises, f'noise_{i}')
                    for i in range(self.num_layers)
                ]

        if truncation < 1:
            style_t = []

            for style in styles:
                style_t.append(truncation_latent + truncation *
                               (style - truncation_latent))

            styles = style_t

        if len(styles) < 2:
            inject_index = self.n_latent
            if styles[0].ndim < 3:
                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            else:
                latent = styles[0]
        else:
            if inject_index is None:
                inject_index = random.randint(1, self.n_latent - 1)

            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            latent2 = styles[1].unsqueeze(1).repeat(
                1, self.n_latent - inject_index, 1)

            latent = torch.cat([latent, latent2], 1)

        out = self.input(latent)
        out = self.conv1(out,
                         latent[:, 0],
                         n_frame,
                         use_noise=use_noise,
                         noise=noise[0])

        skip = self.to_rgb1(out, latent[:, 1])

        i = 1
        for conv1, conv2, noise1, noise2, to_rgb in zip(
                self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2],
                self.to_rgbs):
            out = conv1(out,
                        latent[:, i],
                        n_frame,
                        use_noise=use_noise,
                        noise=noise1)
            out = conv2(out,
                        latent[:, i + 1],
                        n_frame,
                        use_noise=use_noise,
                        noise=noise2)
            skip = to_rgb(out, latent[:, i + 2], skip)

            i += 2

        image = skip

        if return_latents:
            return image, latent
        else:
            if interpolation:
                return image, styles, noise
            else:
                return image, rand_in, rand_rec
